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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Scientific Discovery with the Blue Gene/L

This project succeeded in developing key software optimization tools to bring fundamental QCD calculations of nucleon structure from the Terascale era through the Petascale era and prepare for the Exascale era. It also enabled fundamental QCD physics calculations and demonstrated the power of placing small versions of frontier emerging architectures at MIT to attract outstanding students to computational science. MIT also hosted a workshop September 19 2008 to brainstorm ways to promote computational science at top tier research universities and attract gifted students into the field, some of whom would provide the next generation of talent at our defense laboratories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Induced spin-accumulation and spin-polarization in a quantum-dot ring by using magnetic quantum dots and Rashba spin-orbit effect

The effect of magnetic contacts on spin-dependent electron transport and spin-accumulation in a quantum ring, which is threaded by a magnetic flux, is studied. The quantum ring is made up of four quantum dots, where two of them possess magnetic structure and other ones are subjected to the Rashba spin-orbit coupling. The magnetic quantum dots, referred to as magnetic quantum contacts, are connected to two external leads. Two different configurations of magnetic moments of the quantum contacts are considered; the parallel and the anti-parallel ones. When the magnetic moments are parallel, the degeneracy between the transmission coefficients of spin-up and spin-down electrons is lifted and the system can be adjusted to operate as a spin-filter. In addition, the accumulation of spin-up and spin-down electrons in non-magnetic quantum dots are different in the case of parallel magnetic moments. When the intra-dot Coulomb interaction is taken into account, we find that the electron interactions participate in separation between the accumulations of electrons with different spin directions in non-magnetic quantum dots. Furthermore, the spin-accumulation in non-magnetic quantum dots can be tuned in the both parallel and anti-parallel magnetic moments by adjusting the Rashba spin-orbit strength and the magnetic flux. Thus, the quantum ring with magnetic quantum contacts could be utilized to create tunable local magnetic moments which can be used in designing optimized nanodevices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Inherent limits on optimization and discovery in physical systems

Topological mapping of a large physical system on a graph, and its decomposition using universal measures are proposed. We find inherent limits to the potential for optimization of a given system and its approximate representations by motifs, and the ability to reconstruct the full system given approximate representations. The approximate representation of the system most suited for optimization may be different from that which most accurately describes the full system. - Highlights: • Graph-based approach to analyze full and partial representations of physical systems. • Direct link between a change in the physical system and complexity of the graph. • Inherent limits to the potential for optimization of a general system. • Inherent limits to the reconstruction of full system from partial representations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Tradeoff between energy and error in the discrimination of quantum-optical devices

We address the problem of energy-error tradeoff in the discrimination between two linear passive quantum optical devices with a single use. We provide an analytical derivation of the optimal strategy for beamsplitters and an iterative algorithm converging to the optimum in the general case. We then compare the optimal strategy with a simpler strategy using coherent input states and homodyne detection. It turns out that the former requires much less energy in order to achieve the same performances.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Bandgap Engineering of InP QDs Through Shell Thickness and Composition

Fields as diverse as biological imaging and telecommunications utilize the unique photophysical and electronic properties of nanocrystal quantum dots (NQDs). The development of new NQD compositions promises material properties optimized for specific applications, while addressing material toxicity. Indium phosphide (InP) offers a 'green' alternative to the traditional cadmium-based NQDs, but suffers from extreme susceptibility to oxidation. Coating InP cores with more stable shell materials significantly improves nanocrystal resistance to oxidation and photostability. We have investigated several new InP-based core-shell compositions, correlating our results with theoretical predictions of their optical and electronic properties. Specifically, we can tailor the InP core-shell QDs to a type-I, quasi-type-II, or type-II bandgap structure with emission wavelengths ranging from 500-1300 nm depending on the shell material used (ZnS, ZnSe, CdS, or CdSe) and the thickness of the shell. Single molecule microscopy assessments of photobleaching and blinking are used to correlate NQD properties with shell thickness.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗
DOE OSTI2013

Photo-driven autonomous hydrogen generation system based on hierarchically shelled ZnO nanostructures

A quantum dot semiconductor sensitized hierarchically shelled one-dimensional ZnO nanostructure has been applied as a quasi-artificial leaf for hydrogen generation. The optimized ZnO nanostructure consists of one dimensional nanowire as a core and two-dimensional nanosheet on the nanowire surface. Furthermore, the quantum dot semiconductors deposited on the ZnO nanostructures provide visible light harvesting properties. To realize the artificial leaf, we applied the ZnO based nanostructure as a photoelectrode with non-wired Z-scheme system. The demonstrated un-assisted photoelectrochemical system showed the hydrogen generation properties under 1 sun condition irradiation. In addition, the quantum dot modified photoelectrode showed 2 mA/cm{sup 2} current density at the un-assisted condition.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Asymptotically optimal data analysis for rejecting local realism

Reliable experimental demonstrations of violations of local realism are highly desirable for fundamental tests of quantum mechanics. One can quantify the violation witnessed by an experiment in terms of a statistical p value, which can be defined as the maximum probability according to local realism of a violation at least as high as that witnessed. Thus, high violation corresponds to small p value. We propose a prediction-based-ratio (PBR) analysis protocol whose p values are valid even if the prepared quantum state varies arbitrarily and local realistic models can depend on previous measurement settings and outcomes. It is therefore not subject to the memory loophole [J. Barrett et al., Phys. Rev. A 66, 042111 (2002)]. If the prepared state does not vary in time, the p values are asymptotically optimal. For comparison, we consider protocols derived from the number of standard deviations of violation of a Bell inequality and from martingale theory [R. Gill, e-print arXiv:quant-ph/0110137]. We find that the p values of the former can be too small and are therefore not statistically valid, while those derived from the latter are suboptimal. PBR p values do not require a predetermined Bell inequality and can be used to compare results from different tests of local realism independent of experimental details.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Spectral optimization simulation of white light based on the photopic eye-sensitivity curve

Spectral optimization simulation of white light is studied to boost maximum attainable luminous efficacy of radiation at high color-rendering index (CRI) and various color temperatures. The photopic eye-sensitivity curve V(λ) is utilized as the dominant portion of white light spectra. Emission spectra of a blue InGaN light-emitting diode (LED) and a red AlInGaP LED are added to the spectrum of V(λ) to match white color coordinates. It is demonstrated that at the condition of color temperature from 2500 K to 6500 K and CRI above 90, such white sources can achieve spectral efficacy of 330–390 lm/W, which is higher than the previously reported theoretical maximum values. We show that this eye-sensitivity-based approach also has advantages on component energy conversion efficiency compared with previously reported optimization solutions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

LLRF System Analysis for the Fermilab PIP-II LINAC

Developing long-lived quantum processing units (QPUs) capable of supporting high-fidelity quantum operations is a crucial challenge on the path toward fault-tolerant quantum computing. TESLA-shaped superconducting RF (SRF) cavities, known for photon relaxation times on the order of seconds, provide an excellent foundation for 3D QPUs and quantum memory. This talk presents a novel design that leverages TESLA cavity modes coupled to ancillary transmon qubits, optimized to preserve coherence and control. By carefully engineering the package geometry, optimizing Hamiltonian parameters, and minimizing lossy participation ratios, we achieve photon relaxation times of over 16 ms and 20 ms for the two cavity modes, representing a significant improvement over previous multimode quantum memories. Despite the reduced coupling between the qubit and cavity modes, which is necessary to preserve long lifetimes, the platform supports robust and universal control schemes that are not limited by low coupling strength. We will also discuss how this architecture can lead to scalable, modular quantum computing systems.

Varghese, P. [Fermilab]↗
DOE OSTI2022

Toward a QUBO-Based Density Matrix Electronic Structure Method

Density matrix electronic structure theory is used in many quantum chemistry methods to “alleviate” the computational cost that arises from directly using wave functions. Although density matrix based methods are computationally more efficient than wave function based methods, significant computational effort is involved. Because the Schrödinger equation needs to be solved as an eigenvalue problem, the time-to-solution scales cubically with the system size in mean-field type approaches such as Hartree–Fock and density functional theory and is solved as many times in order to reach charge or field self-consistency. We hereby propose and study a method to compute the density matrix by using a quadratic unconstrained binary optimization (QUBO) solver. This method could be useful to solve the problem with quantum computers and, more specifically, quantum annealers. Our proposed approach is based on a direct construction of the density matrix using a QUBO eigensolver. We explore the main parameters of the algorithm focusing on precision and efficiency. We show that, while direct construction of the density matrix using a QUBO formulation is possible, the efficiency and precision have room for improvement. Moreover, calculations performed with quantum annealing on D-Wave’s new Advantage quantum computer are compared with results obtained with classical simulated annealing, further highlighting some problems of the proposed method. Finally, we also suggest alternative methods that could lead to a more efficient QUBO-based density matrix construction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Probing Interfacial Electronic States in CdSe Quantum Dots using Second Harmonic Generation Spectroscopy

Understanding and rationally controlling the properties of nanomaterial surfaces is a rapidly expanding field of research due to the dramatic role they play on the optical and electronic properties vital to light harvesting, emitting and detection technologies. This information is essential to the continued development of synthetic approaches designed to tailor interfaces for optimal nanomaterial based device performance. In this work, closely spaced electronic excited states in model CdSe quantum dots (QDs) are resolved using second harmonic generation (SHG) spectroscopy, and the corresponding contributions from surface species to these states are assessed. Two distinct spectral features are observed in the SHG spectra, which are not readily identified in linear absorption and photoluminescence excitation spectra. These features include a weak band at 395 6 nm, which coincides with transitions to the 2S1/2 1Se state, and a much more pronounced band at 423 4 nm arising from electronic transitions to the 1P3/2 1Pe state. Chemical modification of the QD surfaces through oxidation resulted in disappearance of the SHG band corresponding to the 1P3/2 1Pe state, indicating prominent surface contributions. Signatures of deep trap states localized on the surfaces of the QDs are also observed. We further find that the SHG signal intensities depend strongly on the electronic states being probed and their relative surface contributions, thereby offering additional insight into the surface specificity of SHG signals from QDs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Risk-Sensitive Control of Pure Jump Process on Countable Space with Near Monotone Cost

In this article, we study risk-sensitive control problem with controlled continuous time pure jump process on a countable space as state dynamics. We prove multiplicative dynamic programming principle, elliptic and parabolic Harnack’s inequalities. Using the multiplicative dynamic programing principle and the Harnack’s inequalities, we prove the existence and a characterization of optimal risk-sensitive control under the near monotone condition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Physical principles of microwave assisted magnetic recording

While the basic physics of Microwave Assisted Magnetization Reversal (MAMR) phenomenon is well established both theoretically and experimentally, its application in a practical magnetic recording environment was so far studied primarily with the help of micromagnetic recording models. In this work, we instead attempt to use analytical formulation and simple numerical models to understand the main challenges as well as benefits that are associated with such a system. It appears that the main difference between the previously introduced theory [G. Bertotti et al., Phys. Rev. Lett. 86, 724 (2001); K. Rivkin et al., Appl. Phys. Lett. 92, 153104 (2008); S. Okamoto et al., J. Appl. Phys. 107, 123914 (2010).] and recording environment is that both the RF and DC magnetic fields are applied at a substantial angle to the anisotropy axis. While the associated symmetry breaking prevents one from describing the reversal process explicitly, it is possible to approximate the solutions well enough to satisfactorily match numerical models both in the case of wire and Spin Torque Oscillator generated RF fields. This approach allows for physical explanation of various effects associated with MAMR such as high gradient of writeable anisotropy and reduction of track width, and offers a clear guidance regarding future optimization of MAMR recording.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗